This chapter provides an overview of the natural roles of different elements in biological systems and the various and extraordinarily subtle ways in which each one is exploited. It starts with a discussion on the physical structure of cells and the inorganic composition of living organisms. The chapter then describes how different elements are taken up selectively by different cells and intracellular compartments. It also explains acid-base catalysis, oxygen atom transfer by molybdenum and tungsten enzymes, and the nitrogen cycle, among others. The chapter concludes by emphasizing that biological metals and metalloproteins have important futures in medicine, energy production, green synthesis, and nanotechnology.

Transition metal ions play crucial roles in the structure and function of numerous proteins, contributing to essential biological processes such as catalysis, electron transfer, and oxygen binding. However, accurately modeling the electronic structure and properties of metalloproteins poses significant challenges due to the complex nature of their electronic configurations and strong correlation effects. Multiconfigurational quantum chemistry methods are, in principle, the most appropriate tools for addressing these challenges, offering the capability to capture the inherent multi-reference character and strong electron correlation present in bio-inorganic systems. Yet their computational cost has long hindered wider adoption, making methods such as Density Functional Theory (DFT) the method of choice. However, advancements over the past decade have substantially alleviated this limitation, rendering multiconfigurational quantum chemistry methods more accessible and applicable to a wider range of bio-inorganic systems. In this perspective, we discuss some of these developments and how they have already been used to answer some of the most important questions in bio-inorganic chemistry. We also comment on ongoing developments in the field and how the future of the field may evolve.

The inorganic chemistry of the cobalt corrinoids, derivatives of vitamin B12, is reviewed, with particular emphasis on equilibrium constants for, and kinetics of, their axial ligand substitution reactions. The role the corrin ligand plays in controlling and modifying the properties of the metal ion is emphasised. Other aspects of the chemistry of these compounds, including their structure, corrinoid complexes with metals other than cobalt, the redox chemistry of the cobalt corrinoids and their chemical redox reactions, and their photochemistry are discussed. Their role as catalysts in non-biological reactions and aspects of their organometallic chemistry are briefly mentioned. Particular mention is made of the role that computational methods - and especially DFT calculations - have played in developing our understanding of the inorganic chemistry of these compounds. A brief overview of the biological chemistry of the B12-dependent enzymes is also given for the reader's convenience.

This chapter explores the synthesis, properties, and applications of materials with at least one dimension in the nanoscale range of 1–100 nm. It introduces nanomaterials by emphasising the importance of controlling particle size and shape to modify properties such as electronic conductivity, optical behaviour, and catalytic activity. Various synthesis methods are examined, including solution-based, vapour-phase, and templated approaches for producing nanomaterials with defined dimensions. The chapter highlights the unique properties of nanoparticles based on their dimensionality, such as zero-dimensional (0D) quantum dots with distinct optical characteristics, one-dimensional (1D) nanowires with enhanced mechanical strength, and two-dimensional (2D) materials like graphene, known for their exceptional electrical conductivity. Lastly, the industrial and biomedical applications of these materials are also discussed, covering fields such as catalysis, sensors, energy storage, and biomedicine, demonstrating their vast potential across various sectors.

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Thermal conductivity is a fundamental material property but challenging to predict, with less than 5% out of about $10^5$ synthesized inorganic materials being documented. In this work, we extract the structural chemistry that governs lattice thermal conductivity, by combining graph neural networks and random forest approaches. We show that both mean and variation of unit-cell configurational properties, such as atomic volume and bond length, are the most important features, followed by mass and elemental electronegativity. We chart the structural chemistry of lattice thermal conductivity into extended van-Arkel triangles, and predict the thermal conductivity of all known inorganic materials in the Inorganic Crystal Structure Database. For the latter, we develop a transfer learning framework extendable for other applications.

The design of functional materials with desired properties is essential in driving technological advances in areas such as energy storage, catalysis and carbon capture1, 2–3. Generative models accelerate materials design by directly generating new materials given desired property constraints, but current methods have a low success rate in proposing stable crystals or can satisfy only a limited set of property constraints4, 5, 6, 7, 8, 9, 10–11. Here we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. Compared with previous generative models4,12, structures produced by MatterGen are more than twice as likely to be new and stable, and more than ten times closer to the local energy minimum. After fine-tuning, MatterGen successfully generates stable, new materials with desired chemistry, symmetry and mechanical, electronic and magnetic properties. As a proof of concept, we synthesize one of the generated structures and measure its property value to be within 20% of our target. We believe that the quality of generated materials and the breadth of abilities of MatterGen represent an important advancement towards creating a foundational generative model for materials design. MatterGen is a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints.

This chapter focuses on the role of metal ions in biological systems, particularly how cells utilise inorganic elements beyond carbon, hydrogen, oxygen, and nitrogen. It begins by exploring cell structure and function, highlighting the selective uptake and compartmentalisation of elements through membranes and ion pumps. The chapter then examines the roles of metal ions such as sodium, potassium, and calcium in transport, signalling, and storage within organisms. A significant section covers iron, discussing its transport via proteins like transferrin and its storage in ferritin. Oxygen transport through haemoglobin and myoglobin is also explored, with emphasis on the coordination chemistry involved. The chapter further addresses enzymatic functions, such as those involving hydrogenases and enzymes responsible for radical rearrangements, in relation to metal cofactors. Lastly, it considers the regulatory role of metals in gene expression and biomineralisation, illustrating the wide range of biological functions influenced by inorganic chemistry.

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Machine learning is revolutionizing chemistry. Beyond the value of predictive models accelerating virtual screening, generative AI aims at enabling inverse design, reversing the compound-to-property prediction paradigm into property-to-compound generation. Chemists now have access to a rich AI toolbox for organic chemistry, including drug discovery. However, the application of these methods to inorganic compounds remains limited by the challenges posed by their intrinsic nature. This Review analyzes how these challenges have been addressed, considering widely diverse systems ranging from molecules to crystals, including transition metal complexes and microporous materials. The analysis focuses on how generative AI methods have evolved towards data-representation-model pipelines that address the full complexity of inorganic compounds, including their chemical composition, geometry, symmetry, and electronic structure. Future directions, like benchmark standardization and the development of synthesizability metrics, are also discussed.

The mechanisms underlying the influence of the surface chemistry of inorganic materials on polymer structures and fracture behaviours near adhesive interfaces are not fully understood. This study demonstrates the first clear and direct evidence that molecular surface segregation and cross-linking of epoxy resin are driven by intermolecular forces at the inorganic surfaces alone, which can be linked directly to adhesive failure mechanisms. We prepare adhesive interfaces between epoxy resin and silicon substrates with varying surface chemistries (OH and H terminations) with a smoothness below 1 nm, which have different adhesive strengths by ~13 %. The epoxy resins within sub-nanometre distance from the surfaces with different chemistries exhibit distinct amine-to-epoxy ratios, cross-linked network structures, and adhesion energies. The OH- and H-terminated interfaces exhibit cohesive failure and interfacial delamination, respectively. The substrate surface chemistry impacts the cross-linked structures of the epoxy resins within several nanometres of the interfaces and the adsorption structures of molecules at the interfaces, which result in different fracture behaviours and adhesive strengths. The mechanisms of adhesion between polymers and inorganic materials are not fully understood. Here, authors demonstrate the clear evidence that the surface chemistry impacts the crosslinked structures of resins near the interfaces, resulting in different fracture behaviours and adhesive strengths.

This chapter describes how medical science is taking advantage of the stranger elements, such as platinum, gold, lithium, arsenic, and synthetic technetium, to diagnose and treat illness. It examines the inorganic complexes in cancer treatment, anti-arthritis drugs, and bismuth in the treatment of gastric ulcers. Next, it tackles the use of lithium in the treatment of bipolar disorders and organometallic drugs in the treatment of malaria. Furthermore, the chapter looks at metal complexes as antiviral agents, and metal drugs that slowly release carbon monoxide to address post-operative stress. It also tackles chelation therapy, imaging agents, and nanoparticles in directed-drug delivery.

Polymers are ubiquitous in modern society. They are used in a variety of appli-cations, ranging from sophisticated - such as electronics - to relatively simple ones like packaging. Inorganic polymers often have certain advantages over their organic counterparts-such as increased thermal stability and unique material properties - and have been an active area of research for many years. The potential technological applications that are imagined for some of these polymers, however, have mostly failed to be realized. This article aims to examine some of the advances in the general field of inorganic polymers, which have been made in the last three years. We also attempted to ascertain whether the promise of these materials will be realized in the near future, especially as advanced polymeric materials

The Gibbs energy, G, determines the equilibrium conditions of chemical reactions and materials stability. Despite this fundamental and ubiquitous role, G has been tabulated for only a small fraction of known inorganic compounds, impeding a comprehensive perspective on the effects of temperature and composition on materials stability and synthesizability. Here, we use the SISSO (sure independence screening and sparsifying operator) approach to identify a simple and accurate descriptor to predict G for stoichiometric inorganic compounds with ~50 meV/atom (~1 kcal/mol) resolution, and with minimal computational cost, for temperatures ranging from 300-1800 K. We then apply this descriptor to ~30,000 known materials curated from the Inorganic Crystal Structure Database (ICSD). Using the resulting predicted thermochemical data, we generate thousands of temperature-dependent phase diagrams to provide insights into the effects of temperature and composition on materials synthesizability and stability and to establish the temperature-dependent scale of metastability for inorganic compounds.

This chapter explores the use of metal-based compounds in treating medical conditions like cancer, arthritis, and bacterial infections. Platinum-based drugs, such as cisplatin, are effective in cancer therapy by disrupting deoxyribonucleic acid (DNA) replication in malignant cells, while gold compounds help manage rheumatoid arthritis by inhibiting inflammation-related enzymes. Bismuth-based drugs are notably successful in treating Helicobacter pylori, a bacterium linked to gastric ulcers and cancer. Lithium's role in managing bipolar disorder is also discussed, though its biochemical mechanisms are still unclear. The chapter concludes with advances in nanoparticle drug delivery systems and the potential of metal complexes in treating malaria and emerging viral infections.

The fulltext of this document is embargoed by the American Chemical Society until March 30, 2024. For full text access please email summit@sfu.c

The NSF Workshop in Quantum Information and Computation for Chemistry assembled experts from directly quantum-oriented fields such as algorithms, chemistry, machine learning, optics, simulation, and metrology, as well as experts in related fields such as condensed matter physics, biochemistry, physical chemistry, inorganic and organic chemistry, and spectroscopy. The goal of the workshop was to summarize recent progress in research at the interface of quantum information science and chemistry as well as to discuss the promising research challenges and opportunities in the field. Furthermore, the workshop hoped to identify target areas where cross fertilization among these fields would result in the largest payoff for developments in theory, algorithms, and experimental techniques. The ideas can be broadly categorized in two distinct areas of research that obviously have interactions and are not separated cleanly. The first area is quantum information for chemistry, or how quantum information tools, both experimental and theoretical can aid in our understanding of a wide range of problems pertaining to chemistry. The second area is chemistry for quantum information, which aims to discuss the several aspects where research in the chemical sciences can aid progress in quantum information science and technology. The results of the workshop are summarized in this report.

This chapter introduces the most important physical techniques that are used to investigate the atomic and electronic structures of inorganic compounds and to study their reactions. It explains the two diffraction methods: X-ray and neutron diffraction. Then it looks at absorption and emission spectroscopies, covering ultraviolet-visible spectroscopy, fluorescence spectroscopy, as well as infrared and Raman spectroscopy. The chapter also describes nuclear magnetic resonance, electron paramagnetic resonance, and Mössbauer spectroscopy. Furthermore, it discusses ionization-based techniques: photoelectron spectroscopy, X-ray absorption spectroscopy, and mass spectrometry. The chapter explains atomic absorption spectroscopy, CHN analysis, X-ray fluorescence elemental analysis, and thermal analysis. Next, it tackles magnetometry and magnetic susceptibility, as well as electrochemical techniques, before looking at scanning probe microscopy and electron microscopy.

Oxidation states are well-established in chemical science teaching and research. We data-mine more than 168,000 crystallographic reports to find an optimal allocation of oxidation states to each element. In doing so we uncover discrepancies between text-book chemistry and reported charge states observed in materials. We go on to show how the oxidation states we recommend can significantly facilitate materials discovery and heuristic design of novel inorganic compounds.

Abstract Metallodrugs correspond to a small portion of all available drugs in the market and, yet, some of them are among the most used and important drugs in modern medicine. However, medicinal inorganic chemistry remains an underestimated area within medicinal chemistry and the main reason is the mislead association of metals to toxic agents. Thus, in this review, the potential of medicinal inorganic chemistry in drug designing is highlighted through a description of the current status of metallodrugs and metallodrug candidates in advanced clinical trials. The broad spectrum of application of metal-based drugs in medicine for both therapy and diagnosis is addressed by the extensive list of examples presented herein.

This chapter explores some recent developments in inorganic materials chemistry. Many of these have come in the area of new materials. The chemistry of high temperature superconducting phases, in terms of the synthesis and structure of these compounds, lies within the area of this text. More recently, the structural chemistry of C 60 in its ionic compounds is also relevant, though the chemistry of this species is developing rapidly in many areas. Consideration of several superconducting systems shows two features in common. They all contain planes of the stoichiometry CuO 2 formed by linking CuO 4 square planes at all vertices, and they all have an average copper oxidation state in excess of 2+. The chapter then looks at the structural chemistry of fullerenes and fullerides.

Inorganic nanocrystal gels retain distinct properties of individual nanocrystals while offering tunable, network structure-dependent characteristics. We review different mechanisms for assembling gels from colloidal nanocrystals including (1) controlled destabilization, (2) direct bridging, (3) depletion, as well as linking mediated by (4) coordination bonding or (5) dynamic covalent bonding, and we highlight how each impacts gel properties. These approaches use nanocrystal surface chemistry or the addition of small molecules to mediate inter-nanocrystal attractions. Each method offers advantages in terms of gel stability, reversibility, or tunability and presents new opportunities for design of reconfigurable materials and fueled assemblies.

This chapter covers various methods used to investigate the structure and properties of inorganic compounds. Diffraction techniques, such as X-ray and neutron diffraction, are essential for determining molecular structures and crystal lattices. Absorption and emission spectroscopies, including UV-visible and infrared, offer insights into electronic structures and vibrational behaviours. Nuclear magnetic resonance (NMR) and electron paramagnetic resonance (EPR) are employed to study electron environments and magnetic properties. Ionisation-based techniques, like mass spectrometry and photoelectron spectroscopy, help identify atomic composition and electronic states. Chemical analysis methods, including atomic absorption spectroscopy and thermal analysis, assist in examining elemental composition. Finally, magnetometry and electrochemical techniques assess magnetic properties and reactions, while microscopy techniques, such as scanning probe and electron microscopy, visualise structures at the atomic level.

Retrosynthesis strategically plans the synthesis of a chemical target compound from simpler, readily available precursor compounds. This process is critical for synthesizing novel inorganic materials, yet traditional methods in inorganic chemistry continue to rely on trial-and-error experimentation. Emerging machine-learning approaches struggle to generalize to entirely new reactions due to their reliance on known precursors, as they frame retrosynthesis as a multi-label classification task. To address these limitations, we propose Retro-Rank-In, a novel framework that reformulates the retrosynthesis problem by embedding target and precursor materials into a shared latent space and learning a pairwise ranker on a bipartite graph of inorganic compounds. We evaluate Retro-Rank-In's generalizability on challenging retrosynthesis dataset splits designed to mitigate data duplicates and overlaps. For instance, for Cr2AlB2, it correctly predicts the verified precursor pair CrB + Al despite never seeing them in training, a capability absent in prior work. Extensive experiments show that Retro-Rank-In sets a new state-of-the-art, particularly in out-of-distribution generalization and candidate set ranking, offering a powerful tool for accelerating inorganic material synthesis.

Two-dimensional (2D) transition-metal carbides and nitrides (MXenes) show impressive performance in applications, such as supercapacitors, batteries, electromagnetic interference shielding, or electrocatalysis. These materials combine the electronic and mechanical properties of 2D inorganic crystals with chemically modifiable surfaces, and surface-engineered MXenes represent an ideal platform for fundamental and applied studies of interfaces in 2D functional materials. A natural step in structural engineering of MXene compounds is the development and understanding of MXenes with various organic functional groups covalently bound to inorganic 2D sheets. Such hybrid structures have the potential to unite the tailorability of organic molecules with the unique electronic properties of inorganic 2D solids. Here, we introduce a new family of hybrid MXenes (h-MXenes) with amido- and imido-bonding between organic and inorganic parts. The description of h-MXene structure requires an intricate mix of concepts from the fields of coordination chemistry, self-assembled monolayers (SAMs) and surface science. The optical properties of h-MXenes reveal coherent coupling between the organic and inorganic components. h-MXenes also show superior stability against hydrolysis in aqueous solutions.

This chapter analyzes individual inorganic elements that are organized into the alkali and alkaline earth metals, transition metals, zinc and iron, and the precious metals, silver, gold, and platinum, and the lanthanide metals. The non-metals studied here are phosphorus and sulfur. The chapter cites other elements which are important to drug development, drug action, and delivery. The chapter analyzes the chemical properties of an element and its biological function and refers to transition metal ions which are found in biological systems and precious metals which are found in drugs. It identifies various phosphorus and sulfur-containing functional groups in drug molecules and highlights the role of these functional groups for either the formulation of the drug into a medicine or the biological action of the drug.

Exploratory synthesis in novel chemical spaces is the essence of solid-state chemistry. However, uncharted chemical spaces can be difficult to navigate, especially when materials synthesis is challenging. Nitrides represent one such space, where stringent synthesis constraints have limited the exploration of this important class of functional materials. Here, we employ a suite of computational materials discovery and informatics tools to construct a large stability map of the inorganic ternary metal nitrides. Our map clusters the ternary nitrides into chemical families with distinct stability and metastability, and highlights hundreds of promising new ternary nitride spaces for experimental investigation--from which we experimentally realized 7 new Zn- and Mg-based ternary nitrides. By extracting the mixed metallicity, ionicity, and covalency of solid-state bonding from the DFT-computed electron density, we reveal the complex interplay between chemistry, composition, and electronic structure in governing large-scale stability trends in ternary nitride materials.